要旨：With the rapid increase in the availability of passive data in the field of transportation, combining machine learning with transportation models has emerged as an important research topic in recent years. This study proposes an entropy Tucker model that integrates (1) a Tucker decomposition technique, i.e., an existing machine learning method, and (2) an entropy maximizing model, i.e., an existing model used for modeling trip distribution in the field of transportation. In addition, an optimization algorithm is presented to empirically identify the proposed model. The proposed model provides a solid theoretical foundation for the machine learning method, substantially improves prediction performance, and provides richer behavioral implications through empirical parameter estimation of travel impedance. We empirically apply the proposed method to evaluate the impacts of changes in the public transport fare structure on the destination choice of public transport users in Hiroshima by using the smart card data collected in Hiroshima, Japan. The estimated values of travel time range from 1.146 to 14.44 JPY/min, which is consistent with that reported in existing studies. The results of scenario analysis with different public transport fare structures suggest that identified changes in trip patterns, revenues, and users’ benefits for the public transport operator are considerably different between the conventional entropy model and the proposed entropy Tucker model. Further, we confirm that the users’ benefits vary based on the time of day. These obtained results confirm the importance of considering the heterogeneous preferences of users in economic appraisals.
要旨：Older people are less mobile than young people are. Population aging thus means more people would be trapped in locations affected by a shock, preventing the economy from smoothing out spatial differences in labor market outcomes. However, the existence of a large share of immobile workers may mitigate their welfare effects by delaying the capital supply adjustment that would be caused by a flow of workers. In order to study how population aging affects the welfare effects of a local shock, this paper develops a dynamic spatial specific-factor model with demographics that change dynamically depending on fertility rates. Individuals decide where to live and whether to work. Their choices vary over the life cycle because the expected working lifetime and fundamentals (e.g., mobility costs) vary with demographic factors. Hence, aggregate labor adjustment depends on the economy’s age structure. Forward-looking landlords accumulate location-specific capital, and the dynamics of labor and capital interact with each other. I apply the model to Japan and find that population aging can mitigate the welfare loss of workers in a location affected by a negative shock.
要旨：This paper theoretically investigates the relationship between costs for telework and the location of firms and households in a city. I extend the model of Ogawa and Fujita (1980) by introducing two types of companies with different technologies; one is teleworking company hiring more teleworkers and the other is an office company hiring more on-site workers. Only on-site workers conduct face-to-face communication with other firms and incur communication costs. This paper shows that (i) when telework cost decreases, the first teleworking companies appear in either of two candidate locations: the edge of the existing central business districts (called CBD fringe) or the edge of the residential districts (called urban fringe). If face-to-face communication cost is high, and commuting cost and the ratio of labor input for teleworkers in telework companies are low, then the first telework company is located at the CBD fringe; otherwise at the urban fringe. (ii) When the telework cost further decreases, the number of telework companies, wage, and welfare increase. In contrast, bid rent and city boundaries decrease. Former empirical researches showed different evidence for the location of primary telework companies; one is nearby CBD and the other is in the suburban area. However, this is the first paper to demonstrate both results in a model and to show the difference depends on the several key parameters: face-to-face communication cost, commuting cost, and the ratio of labor input for teleworkers in telework companies.
要旨：Economies transform at an uneven pace: San Jose’s meteoric rise coexists with Detroit’s slow decline. This paper develops a dynamic overlapping generations model of economic geography to explain variation in structural transformation across space and time. In the model, historical exposure to different industries creates persistence in occupational structure, and non-homothetic preferences and differential productivity growth lead to different rates of structural transformation. Despite the heterogeneity across locations, sectors, and time, the model remains tractable and is calibrated to match metropolitan area data for the U.S. economy from 1980 to 2010. The calibration allows us to back out measures of upward mobility and inequality, thereby providing theoretical underpinnings to the Gatsby Curve. The counterfactual analysis shows that structural transformation has substantial effects on mobility: if there were no productivity growth in the service sector, income mobility would be 6 percent higher, and if amenities were equalized across locations, it would rise by 10 percent.
要旨：We propose a scalable regression model with spatially and temporally varying co- efficients based on Moran’s eigenvectors and efficient computation algorithms. Regression models that consider spatiotemporal non-stationarity are important because many real-world datasets, such as housing prices, are tied to geographical and tempo- ral locations. Although geographically weighted regression (GWR) and its variants are widely used to model spatially varying coefficients, they cannot handle large datasets. We employ an alternative modelling method of spatially varying coefficients based on Moran’s eigenvectors and extend it to handle large spatiotemporal datasets. Additionally, we introduce a scalable learning algorithm that exploits the model structures based on the Kalman filter and the expectation—maximisation algorithm. Our scalable algorithm is efficient even for large datasets that cannot be handled by GWR. To evaluate the performance of the proposed model, we applied it to a housing market dataset collected in Tokyo, Japan. The results show that the predictive performance of the proposed model is comparable to that of GWR while increasing the computational speed. Moreover, larger datasets can accelerate the algorithm convergence.
要旨： We study the impact of telecommuting in a monocentric city which produces (i) a tradable consumption good using skilled and unskilled labor and (ii) a non-tradable consumer service provided by unskilled workers at the city center to the skilled workers. Commuting costs are proportional to wages. When the WFH share is low, the skilled reside near the CBD and all workers earn more under WFH. By contrast, a high WFH share lowers both wages and leads the skilled to reside in the suburbs. Telecommuting leads to lower urban costs in the latter case, but not in the former. We then consider two cities that have different productivities. WFH allows skilled workers of the more productive city to reside in the less productive city where housing is cheaper while keeping their job in the more productive city. The flow of this type of inter-city commuters first increases and, then, decreases with the WFH share. Likewise, skilled workers of the less productive city may take a job in the more productive city while keeping their residence in the less productive city. The flow of the second type of inter-city commuters increases with the WFH share. For these commuting patterns to arise, the two employment centers must be connected by a link that allows workers to travel at relatively low costs.